Credit supply and housing speculation

Atif Mian, Amir Sufi 19 August 2018



Charles P. Kindleberger, who was the world’s leading expert on financial crises, wrote that “asset price bubbles depend on the growth in credit” (Kindleberger and Aliber 2005). Nobel prize winner Vernon Smith described evidence from experimental settings showing that that the size of a bubble increased when individuals were allowed to borrow (Porter and Smith 1994). Economic theorists have taken this lesson to heart, writing down models in which easier credit helps fuel asset prices through an increase in speculative buying (Allen and Gorton 1993, Allen and Gale 2000). 

A core idea in the theory of credit and bubbles is that easier credit allows optimists with high asset valuations to aggressively buy assets, and therefore boost the price (Geanakoplos 2010, Simsek 2013). Even if optimists form a small part of the overall population, easier credit can allow this small group to have a large effect on the market. Further, if the optimists suddenly lose access to credit, the price of the asset will collapse before more pessimistic individuals can be induced to buy the asset. As a result, fluctuations in credit availability increase the amplitude of fluctuations in asset prices.

Our recent study tests this idea, focusing on the boom and bust in house prices from 2000 to 2010 in the US (Mian and Sufi 2018). The study focuses on a natural experiment: the sudden acceleration of the private label mortgage securitisation (PLS) market in the late summer of 2003. The sudden rise in the PLS market, which was part of the broader global rise in shadow banking during this period, disproportionately reduced the cost of financing by lenders that did not traditionally rely on deposit financing for mortgage lending. The study shows that lenders who traditionally relied on non-deposit financing, such as CountryWide and Ameriquest Mortgage Company, suddenly boosted mortgage lending in the late summer of 2003, just as the PLS market accelerated.

To test the effect of this sudden increase in credit availability on the housing market, we exploit variation across geographic areas in the US in the location of these lenders as of 2002. Zip codes where lenders traditionally relied on non-deposit financing witnessed a sudden and large relative increase in mortgage lending just as the PLS market accelerated in 2003. Our study shows several results that suggest this is a clean experiment – the sudden and large expansion of mortgage lending in these zip codes was due to the acceleration of the PLS market, as opposed to some other factor such as a change in income prospects or beliefs about house prices among those living in these zip codes.

Consistent with models in which credit availability affects asset prices, the sharp rise in mortgage lending in these zip codes generated a boom and bust in house prices. In fact, exposure of a zip code to non-traditional lenders in 2002 predicted the severity of the collapse in house prices from 2006 to 2010. 

Furthermore, US cities that had greater exposure to these lenders were more likely to experience a simultaneous increase in both house prices and construction activity during the boom. The presence of such bubble cities, such as Las Vegas and Phoenix, has puzzled economists because in most standard models the ability to easily construct more housing units should put a lid on house price growth. The results of our study suggest that easier credit was a crucial ingredient in explaining bubble cities that had both house price and construction booms. We further show that these cities witnessed a particularly painful bust from 2006 to 2010.

A unique advantage of our study is the ability to track the marginal buyers of homes that were brought into the market by easier credit. Zip codes more exposed to the acceleration of the PLS market witnessed a substantial increase in transaction volume from 2003 to 2006, and this increase in volume was almost completely driven by flippers (i.e. individuals that buy and sell multiple homes in a short period of time). Such flippers were a small fraction of the overall population – by our estimate, flippers made up less than 1% of the overall adult population in 2005 and 2006. Despite being a small part of the overall population, flippers had a disproportionate effect on the housing market because they were able to easily obtain credit. 

The results support models in which easier credit can boost asset prices by giving a small group of aggressive buyers the ability to affect the overall market. In the presence of easy credit, it is not necessary for there to be a widespread increase in optimism about the housing market to generate a large increase in house prices.

Evidence from the Michigan Survey of Consumers supports this conclusion. As has been shown in previous research (Piazzesi and Schneider 2009), the fraction of the overall population who said “it is a good time to buy a home” actually declined substantially from 2003 to 2006 during the heart of the housing boom. We add to this evidence by showing that the share of individuals saying “now is a good time to buy a home” declined most in cities that experienced a large rise in house prices fuelled by the PLS market. On average, individuals became increasingly pessimistic about the housing market in cities where the PLS market fuelled a trading frenzy by flippers. Easy credit allowed a small group of individuals to boost house prices in some cities even though the average individual in these cities soured on the housing market.

Flipping fuelled by the PLS market was a crucial factor that instigated the mortgage default crisis. As early as 2007, flippers in zip codes most exposed to the PLS market had default rates above 20%. The share of all mortgage defaults from zip codes most exposed to the PLS market increased in 2007. By 2008 and 2009, defaults were rising throughout the country, but the evidence suggests that the mortgage default crisis was triggered by defaults emanating from the PLS market.

The bust also provides important lessons for the interaction of credit and asset prices. While almost all buyers in zip codes most exposed to the PLS market used a mortgage to buy a home from 2003 to 2006, the share of cash-buyers increased sharply in 2007 and afterward. This pattern is consistent with the idea that prices collapsed in part because tighter credit prevented optimists from buying homes during the sell-off, which meant more pessimistic cash-buyers became the marginal price setters. Loose credit boosted prices during the boom, and tight credit exacerbated the bust. Credit fluctuations and asset price fluctuations are closely connected.


Allen, F and D Gale (2000), “Bubbles and crises," The Economic Journal 110(460): 236-255.

Allen, F and G Gorton (1993), “Churning bubbles," The Review of Economic Studies 60(4): 813-836.

Geanakoplos, J (2010), “The leverage cycle," NBER Macroeconomics Annual 24(1): 1-66.

Kindleberger, C P and R Z Aliber (2005), Manias, panics and crashes: A history of financial crises, Palgrave Macmillan.

Mian, A and A Sufi (2018), “Credit Supply and Housing Speculation,” NBER Working Paper 24823.

Piazzesi, M and M Schneider (2009), “Momentum traders in the housing market: survey evidence and a search model," American Economic Review 99(2): 406-11.

Simsek, A(2013), “Belief disagreements and collateral constraints," Econometrica 81(1): 1-53.



Tags:  house prices, boom and bust, US mortgage market, asset price bubbles, credit

Professor of Economics, Princeton University

Associate Professor of Finance, University of Chicago Booth School of Business


CEPR Policy Research